Session 3
Basics of Species Distribution Models (SDMs)
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Species distribution models are instrumental in predicting and
understanding the geographic ranges of species. In this session, we will
delve into the use of dismo, mgcv and
randomForest R packages, covering the basics of species
distribution modelling. This will be a quick run-though over the theory
and workflow of how to construct SDMs using observational data, and
useful resources to help you along the way. Participants will learn how
to construct predictive models and assess habitat suitability for
species.
Theory and Concept
Species Distribution Models (SDMs) are fundamental tools in ecology and conservation biology, aiming to understand and predict the spatial distribution of species based on environmental variables. At its core, SDMs are grounded in the ecological niche theory, which suggests that species occupy specific ecological niches characterised by environmental conditions such as temperature, water quality, and habitat features. By quantifying the relationship between species occurrence records and environmental variables through statistical modeling techniques, SDMs provide insights into the ecological requirements and habitat preferences of species.
These models typically utilise various algorithms, including MaxEnt, Random Forest, and Generalized Linear Models, to predict species distributions across geographic areas. We will quickly go through an example dataset, and how different model structures and algorithm can drastically change your predicted distribution. SDMs have diverse applications, from assessing the potential impacts of climate change on species distributions to informing conservation planning and management strategies. However, they also come with limitations and uncertainties, including data quality issues, assumptions about species-environment relationships, and challenges in extrapolating predictions to novel environmental conditions. Continuous refinement and integration of multiple data sources and modeling approaches are essential for improving the accuracy and reliability of SDMs, thereby enhancing their utility in addressing pressing ecological and conservation challenges. In this session, we will run though a quick example of how SDMs are created in R, and the complexities of their development and interpretation.
Here we will provide a quick tour of SDMs, but if you want a more in-depth look at what goes into building and refining a SDM, check out some of these online sources (SDM Intro,terra vignette and Ecocommons). There is also a wealth of literature on the subject that you can refer to, including this curated list of R packages that you can use to explore these analyses.
In this session we will use occurrence data on Oceanic Whitetip Sharks (Carcharhinus longimanus) from the Indian Ocean to model their distribution using five commonly used SDM algorithms. We will also finally quickly introduce a simple ensemble model workflow to show how you can integrate outputs from multiple algorithms to get better predictions of distribution. We have provided code to replicate the analysis and plotting, but as we will have limited time during this session, we dont expect everyone to run these code. You can run them on your own time to see how it works!
Basic steps of building SDMs
Building a SDM is an iterative process, and typically involves several steps. The paper Zurell et al. 2020 provides a comprehensive step by step run through of a standard protocol on how to develop SDMs and how to report them in papers to allow for reproducibility. Here’s a quick overview:
Overview/Conceptualisation - One of the key steps in developing SDMs is first conceptualising the objectives, available data, and the relevance of SDMs for your research question. This includes understanding if SDMs are going to tell you what you need to answer your hypothesis. Similarly, if you do not have access to relevant data, then the outputs of SDMs may not be useful (the old GIGO adage: garbage in, gargabe out!). In this step, it is worth overviewing and conceptialising the objectives of the modelling excercise, and asscertain if this model is relevant for the taxon, location, spatial and temporal scale of your study system.
Data Collection, preperation and exploration - The next step we gather occurrence data for the species of interest (e.g., presence-only or presence-absence data) and environmental variables (e.g., temperature, bathymetry) that may be biologically relevant for the study area and the species of interest. Organise and clean the data, ensuring that it’s in a suitable format for analysis. This may involve handling missing values, converting data types, and standardizing units. Explore the relationships between species occurrence and environmental variables using visualization techniques such as scatter plots, histograms, and correlation matrices.
Model selection, fitting and training - We
then choose an appropriate modeling technique for your data. Common SDM
algorithms in R include MaxEnt (using the dismo package),
Generalized Linear Models (GLMs) or Generalized Additive Models (GAMs)
(using the mgcv package), and Random Forest (using the
randomForest package). We then Split the data into training
and testing sets needed to evaluate model performance further down the
track. We finally fit the chosen model using the training data. We will
cover some of these techincal steps later in this session.
Model assessment and exploration - Once models have been fit, we assess the performance of the model using evaluation metrics such as Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision-Recall Curve (AUC), or True Skill Statistic (TSS). We also explore the response curves for the model to identify how each explanatory predictor impacts the probability of presence of your species. Response curves and variable importance plots can tell you if the predictor variables used are relevant, or having any influence on the model.
Model prediction and visualisation - Once
the model is trained and evaluated, use it to predict species
distributions across the study area based on environmental variables.
Visualize the predicted species distribution map using mapping packages
like terra or ggplot2, along with additional
spatial data if necessary. Validate the predicted distributions against
the testing subset of data, independent occurrence data or expert
knowledge to verify the accuracy and reliability of the model.
Refinement and Iteration - Refinement is key in SDMs, and you can often go through 10s of iterations before you have a properly fitted model that provides accurate estimates of species presence and response curves. Iterate through the modeling process, refining the model and incorporating additional data or variables as needed to improve accuracy and robustness.
Data preperation
Here we will go through a quick workflow in R to enable a very simple SDM, from sourcing occurrence data all the way to building ensemble models. Note, this is an overly simplistic example just to get everyone up to speed on how SDMs operate using occurrence data. We will go through how this modelling workflow can be modified to use movement data to inform distribution models in the next session.
Occurrence data
Absence or Pseudo-absence data
Environmental predictors
